Model Monitoring

Model monitoring refers to the process of continuously assessing the performance, behavior, and predictions of machine learning models in production. It involves tracking key metrics such as accuracy, precision, recall, and response time to ensure that the model remains effective over time. Common characteristics include anomaly detection, data drift detection, and alerting mechanisms that notify stakeholders of performance degradation. This practice is crucial in identifying issues early, enabling timely interventions, and maintaining model reliability. Use cases often involve financial forecasting, fraud detection, and recommendation systems where consistent performance is critical.